Research Insights on Redemptive Usecases  
with CHAI.
Missional AI 2025.

Belief-based AI Fairness  
with CHAI.
Pre-print.

Introduction to AI at Accord  
with CHAI.
Accord 2023.

Preference Learning Algorithms Do Not Learn Preference Rankings
with Angelica Chen, Sadhika Malladi, Lily H. Zhang, Xinyi Chen, Rajesh Ranganath, Kyunghyun Cho.
Neurips 2024.

Ontology of Belief Diversity: A Community-Based Epistemological Approach    
with Tyler Fischella, Erin van Liemt.
AIES 2024.

Getting Aligned on Representational Alignment  
with multiple authors.
ArXiV 2023.

Leveraging Contextual Counterfactuals Toward Belief Calibration  
with Michael S. Lee, Sherol Chen
ICML 2023.

Beyond Worst-Case Dimensionality Reduction for Sparse Vectors  
with Sandeep Silwal and David Woodruff.
ICLR 2025.

Quantifying Knowledge Distillation Using Partial Information Decomposition  
with Pasan Dissanayake, Faisal Hamman, Barproda Halder, Ilia Sucholutsky, Sanghamitra Dutta.
AIStats 2025.

Optimized Tradeoffs for Private Prediction with Majority Ensembling  
with Shuli Jiang, Gauri Joshi.
TMLR 2024.

Optimal Scalarizations for Sublinear Hypervolume Regret  
Solo paper.
Neurips 2024.

The Vizier Gaussian Process Bandit Algorithm  
with Xingyou Song, Chansoo Lee, Emily Fertig, Tzu-Kuo Huang, Lior Belenki, Greg Kochanski, Setareh Ariafar, Srinivas Vasudevan, Sagi Perel, Daniel Golovin.
ArXiv 2024.

Predicting from Strings: Language Model Embeddings for Bayesian Optimization  
with Tung Nguyen, Bangding Yang, Chansoo Lee, Jorg Bornschein, Yingjie Miao, Sagi Perel, Yutian Chen, Xingyou Song.
ArXiV 2024 (In Submission).

Quantifying Spuriousness of Biased Datasets Using Partial Information Decomposition
with Barproda Halder, Faisal Hamman, Pasan Dissanayake, Ilia Sucholutsky, Sanghamitra Dutta.
ICML 2024.

Adaptive Regret for Bandits Made Possible: Two Queries Suffice  
with Zhou Lu, Xinyi Chen, Fred Zhang, David Woodruff, Elad Hazan.
ICLR 2024.

Set Learning for Accurate and Calibrated Models  
with Lukas Muttenthaler, Robert A. Vandermeulen, Thomas Unterthiner, Klaus-Robert Müller.
ICLR 2024.

Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products  
with Tamas Sarlos, Xingyou Song, David Woodruff
Neurips 2023.

Computing Approximate ell_p Sensitivities  
with Swati Padmanabhan, David Woodruff.
Neurips 2023.

Robust Algorithms on Adaptive Inputs from Bounded Adversaries  
with Yeshwanth Cherapanamjeri, Sandeep Silwal, David Woodruff, Fred Zhang, Samson Zhou.
ICLR 2023.

Towards Learning Universal Hyperparameter Optimizers with Transformers  
with Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'aurelio Ranzato, Sagi Perel, Nando de Freitas.
Neurips 2022. [Google AI Blog]

Optimal Query Complexities for Dynamic Trace Estimation  
with David Woodruff, Fred Zhang.
Neurips 2022 (Spotlight).

Leveraging Initial Hints for Free in Stochastic Linear Bandits  
with Ashok Cutkosky, Chris Dann, Abhimanyu Das.
ALT 2022.

Optimal Sketching for Trace Estimation  
with Shuli Jiang, Hai Pham, David Woodruff.
Neurips 2021 (Spotlight).

One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks  
with Atish Agarwala, Abhimanyu Das, Brendan Juba, Rina Panigrahy, Vatsal Sharan, Xin Wang.
ICLR 2021.

Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization  
with Daniel Golovin.
ICML 2020.

Gradientless Descent: High-Dimensional Zeroth-Order Optimization   [slides]
with Daniel Golovin, John Karro, Greg Kochanski, Chansoo Lee, Xingyou Song.
ICLR 2020 (Spotlight).

Span Recovery for Deep Neural Networks with Applications to Input Obfuscation  
with Rajesh Jayaram, David Woodruff.
ICLR 2020.

Regularized Weighted Low Rank Approximation  
with Frank Ban, David Woodruff.
Neurips 2019.

Solving Empirical Risk Minimization in the Current Matrix Multiplication Time  
with Zhao Song, Yin Tat Lee.
COLT 2019.

Optimal Sequence Length Requirements for Phylogenetic Tree Reconstruction with Indels  
with Arun Ganesh.
STOC 2019.

Using INC Within Divide-and-Conquer Phylogeny Estimation  
with Thien Le, Aaron Sy, Erin Molloy, Satish Rao, Tandy Warnow.
AlCoB 2019.

New Absolute Fast Converging Phylogeny Estimation Methods with Improved Scalability and Accuracy  
with Satish Rao, Tandy Warnow.
WABI 2018.

Convergence Results for Neural Networks via Electrodynamics  
with Rina Panigrahy, Sushant Sachdeva.
ITCS 2018.

Forbidden Directed Minors and Kelly-width  
with Shiva Kintali.
Theoretical Computer Science 2017, Vol. 662.